How to Become a Data Scientist – By Ryan Orban, VP of Operations and Expansion, Galvanize
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Galvanize VP Ryan Orban explain how and why to beomce a data scientist.
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How to Become a Data Scientist – By Ryan Orban, VP of Operations and Expansion, Galvanize
- 1. HOW TO BECOME A DATA SCIENTIST Wednesday, April 15th 6:15-8:30p 1062 Delaware St.
- 2. Co-Founder & CEO Zipfian Academy Ryan Orban @ryanorban VP of Operations & Expansion Galvanize
- 3. Why are we talking about data science?
- 6. Technology Year Capacity (GB) Cost per GB (USD) 1992 0.08 $3,827.20 1997 2.1 $157.00 2002 80 $3.74 2007 750 $0.35 2012 3,000 $0.05 Source: http://www.jcmit.com/diskprice.htm
- 7. Technology Source: http://www.jcmit.com/diskprice.htm 0 1000 2000 3000 4000 1992 1997 2002 2007 2012 Capacity (GB) Cost per GB (USD)
- 11. Data Science Job Growth Source: LinkedIn Analytics
- 13. Enter the Data Scientist
- 14. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 17. • Ask good questions. What do we not know? What would we like to know? • Define and test an hypothesis. Run experiments with data. • Scrape, munge, transform, and clean data. Instrument. • Explore Data, Discover Unknowns • Model Data, Understand Data Relationships • Create Data Products. Tell Relevant Business Stories. 10 Things [most] Data Scientists Do
- 18. What do people look for in a data scientist?
- 20. T-Shaped Skillset Machine Learning, Statistics, Domain Knowledge Softw are EngineeringBusiness Acum en Distributed Com puting Com m unication
- 22. How to I become a data scientist?
- 24. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 25. Mathematics Statistics Mathematics & Statistics Statistical Distributions (Non) Parametric Tests Significance & Hypothesis Testing Bayesian Methods Linear Algebra Multivariable Calculus Graph Theory Probability
- 27. Learn the tools of the trade. 2
- 29. Python R Julia Java C++/GoScala Prototyping Production Software Engineering
- 30. Python R Julia Java C++/GoScala Prototyping Production Software Engineering
- 31. Python R Julia Java C++/GoScala Prototyping Production Software Engineering
- 32. Learn to Code
- 33. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 34. Distributed Computing Supervised (SVM, Random Forest) NLP / Information Retrieval Algorithms & Data Structures Data Visualization Data Munging Machine Learning & Software Engineering Machine Learning Software Engineering Validation, Model Comparison Unsupervised (Clustering, Topic Modeling)
- 36. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 37. DataTau GitHub
- 38. Math & Stats Computer Science Domain Expertise Machine Learning Software Engineering Research Unicorn Data Science
- 39. DATA SCIENCE HIERARCHY OF NEEDS Mathematics, Statistics, Probability FOUNDATIONAL Software Engineering, Machine Learning, ETL, Data Transformation, SQL FUNCTIONAL Distributed Computing: Hadoop, Cascading, Spark, GraphLab SCALABLE Make an impact, communicate results, identify business objectives EMOTIONAL
- 40. Learning data science on your own can be long and winding road.
- 42. Open-Source Data Science Masters
- 45. SlideRule
- 46. Building the modern, educational campus for digital innovators and entrepreneurs
- 49. BOOT CAMPS LACK A LARGER ECOSYSTEM ISSUE #3
- 50. AN URBAN CAMPUS & EDUCATION PLATFORM
- 51. Galvanize provides immersive educational programs in web development and data science. IMMERSIVE EDUCATIONAL PROGRAMS Full stack development curriculum provided over an immersive, six month full-time program • Strong Test-Driven Development (TDD)/agile software development environment • No prior coding experience required FULL STACK Data science program (previously Zipfian academy) taught over 12-week immersive schedule • One year accredited program resulting in Masters degree (Engineering/Big Data) DATA SCIENCE PROGRAM
- 52. Timeline STRUCTURED CURRICULUM HIRING DAY CAPSTONE PROJECT GRADUATION 1 8 11 12 INTERVIEW PREP Program Timeline
- 53. The Program • Project-based curriculum with real datasets, solving actual problems • Guest lectures from leaders in the field • Personal mentorship to help students grow • Capstone project with company partners • Vast network of hiring partners and events
- 56. Backgrounds Educational Background BS MS PhD 0 4 8 12 16
- 57. Backgrounds Disciplines Software Engineering Analysts Finance/Economics Engineering Physics Physical Sciences Mathematics Statistics Astronomy Linguistics Professional Poker 0 2 4 6 8
- 58. 94% Placement Rate94% Placement $115k avg. salary
- 59. Hiring Partners
- 60. • Working knowledge of programming • Background in a quantitative discipline, strong fundamentals • Comfortable with mathematics and statistics • Child-like curiosity What We Look For
- 61. Data Science Immersive Denver, July 6th
- 62. • Present a guest lecture or share a data story • Donate datasets and propose projects • Sponsor a scholarship • Attend one of our Hiring Days Get Involved
- 63. • Instructors (Full-time & Part-Time) • Outcomes Manager • Student Services • Community Coordinator • Membership Manager We’re Hiring!
- 64. Data Science Immersive GalvanizeU Full-stack Web Development Weekend Workshops
- 65. A Practical Intro to Data Science http://bit.ly/learndatascience
- 66. THANK YOU RYAN ORBAN| VP OF OPERATIONS & EXPANSION @RYANORBAN www.galvanize.com